Determination of burn severity models ranging from regional to national scales for the conterminous United States

Identifying meaningful measures of ecological change over large areas is dependent on the quantification of robust relationships between ecological metrics and remote sensing products. Over the past several decades, ground observations of wildfire and prescribed fire severity have been acquired acro...

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Bibliographic Details
Published in:Remote sensing of environment 2021-09, Vol.263, p.112569, Article 112569
Main Authors: Picotte, Joshua J., Cansler, C. Alina, Kolden, Crystal A., Lutz, James A., Key, Carl, Benson, Nathan C., Robertson, Kevin M.
Format: Article
Language:English
Subjects:
Classification
Classification systems
Decision trees
dNBR
Goodness of fit
Ground-based observation
LANDFIRE
Landsat
Landsat satellites
Linear regression
MTBS
NBR
Prescribed fire
Reflectance
Regression analysis
Regression models
Remote sensing
Sigmoid regression
Spectral reflectance
Vegetation
Vegetation type
Wildfires
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Summary:Identifying meaningful measures of ecological change over large areas is dependent on the quantification of robust relationships between ecological metrics and remote sensing products. Over the past several decades, ground observations of wildfire and prescribed fire severity have been acquired across hundreds of wildland fires in the United States, primarily utilizing the Composite Burn Index (CBI) plot protocol. These observations have been coupled to spaceborne passive spectral reflectance indices (e.g. Landsat-derived variations of the Normalized Burn Ratio [NBR]) to produce regression models describing their relationship. Here we develop regression models by vegetation type for multiple vegetation classification systems representing a range of spatial scales, and a decision tree framework for evaluating these regression models. Our overall goals were to determine which scale of ecological classifications provided the best estimate of burn severity from Landsat data and how to choose the best regression model. We aggregated a total of 6280 CBI plots for 234 wildland fires that burned between 1994 and 2017 and produced Landsat-derived NBR and differenced NBR (dNBR) values for each plot. We then calculated best fit linear or higher order regression equations between CBI and NBR/dNBR for each landcover classification system from smallest to largest scale: LANDFIRE Biophysical Settings (BPS), National Vegetation Classification macrogroup (NVC) landcover classifications, Omernick III, II, and I ecoregions, LANDFIRE Fire Regime Groups (FRG), and the entire conterminous United States (CONUS) dataset. The CONUS regression model goodness of fit was moderate (R2 = 0.55, P 
ISSN:0034-4257
1879-0704
DOI:10.1016/j.rse.2021.112569